{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K4CIBcUlqqWs",
        "outputId": "c5294054-5d94-44da-c23d-c6ac59d6e748"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive/\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount(\"/content/drive/\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ls"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HNeQEeR-rP6P",
        "outputId": "ac5301c6-d9a0-4c6a-9d22-ba725e662649"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[0m\u001b[01;34mdrive\u001b[0m/  \u001b[01;34msample_data\u001b[0m/\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cd \"/content/drive/MyDrive/DialoGraph_ICLR21\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FQ7JwypkrwBc",
        "outputId": "eb8b2394-d9d9-4d18-89de-3be1d9b70c01"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[Errno 2] No such file or directory: '/content/drive/MyDrive/DialoGraph_ICLR21'\n",
            "/content\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ls"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KZ5oWBScr1x7",
        "outputId": "975c55f3-ba92-4b41-99bf-d652a296b6b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "annotated_user_da_with_span_full.json  pip_requirements.txt\n",
            "\u001b[0m\u001b[01;34mcocoa_folder\u001b[0m/                          README.md\n",
            "conda_enviroment.yml                   setup_requirements.sh\n",
            "CraigListBergain.csv                   \u001b[01;34msrc\u001b[0m/\n",
            "\u001b[01;34mdata\u001b[0m/                                  strategy_vector_data_FULL_Yiheng.pkl\n",
            "data.json                              testListFile.json\n",
            "LICENSE                                valListFile.json\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import pickle\n",
        "import re"
      ],
      "metadata": {
        "id": "nDQq0qJkr2-E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "d = pd.read_pickle(\"strategy_vector_data_FULL_Yiheng.pkl\")"
      ],
      "metadata": {
        "id": "oJagtfFewNtD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "d.keys()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oD7olnj6wjqg",
        "outputId": "e5953388-852d-44d2-dc10-cb3e2b26c97d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['train', 'strategies2colid', 'dialacts2id', 'test', 'valid', 'result_dialogs_dynamic', 'result_dialogs_no', 'result_dialogs_static', 'feature_weights', 'word2id'])"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "d['dialacts2id'].keys()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "evl6qOoayazQ",
        "outputId": "5fcfcb19-3c88-4123-c381-d0d22c6f1ca9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['inform', 'agree', 'intro', '<reject>', 'counter-price', 'vague-price', 'inquiry', 'unknown', 'insist', '<accept>', '<offer>', 'disagree', 'init-price', '<quit>', '<start>'])"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "d['dialacts2id']['intro']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H8doNEvf91XU",
        "outputId": "1e0c1b76-be35-4ab4-8697-a2621f66078d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "d['strategies2colid'].keys()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yKlGMo-CyFcq",
        "outputId": "c2dacd85-b287-4e1e-a23a-d3bde37f070f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['agent_id', 'assertive_count', 'third_person_singular', 'hedge_count', 'neg_sentiment', 'trade_in', 'liwc_informal', 'personal_concern', 'friend', 'liwc_certainty', 'first_person_singular_count', 'third_person_plural', 'politeness_greet', 'number_of_diff_dic_neg', 'factive_count', 'politeness_gratitude', 'number_of_diff_dic_pos', 'family', 'politeness_please', 'pos_sentiment', 'propose', 'first_person_plural_count', '<start>'])"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "d['train'][0].keys()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IgYmV3sdwkCC",
        "outputId": "1aeb2ee2-d2ce-4e2f-85a8-72a19cf0f239"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['agent_list', 'utterance', 'strategies', 'ratio', 'dial_acts', 'uuid', 'scene', 'raw_data', 'strategies_vec', 'dial_acts_vec', 'toks_space', 'toks_bert', 'scene_space', 'scene_bert', 'ratio_bucket'])"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "d['train'][0]['scene']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "id": "3Bo5xyKuw2yT",
        "outputId": "6d1330db-70cc-4e85-efa9-cd6521508b66"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'phone charge two devices simultaneously on the go . this vehicle charger with an additional usb port delivers enough power to charge two devices at once . the push button activated led connector light means no more fumbling in the dark trying to connect your device . auto detect ic technology automatically detects the device type and its specific charging needs for improved compatibility . and the built in indicator light illuminates red to let you know the charger is receiving power and the power socket is working properly . verizon car charger with dual output micro usb and led light'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "d['train'][5]['agent_list']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2_EZfadJzrtt",
        "outputId": "aaee9acd-1b59-4c48-dcdf-d689b35ab7f4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[-1, 1, 0, 1, 0, 1, 1, 0]"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "d['train'][767]['utterance']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ILx8MG_Nx9PC",
        "outputId": "9789a098-c8e1-4271-9d4c-315fbd9ab719"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['<start>', 'hi !', '<offer>', '<reject>', '<quit>']"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "d['train'][1]['utterance']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X8EeXu3C-fC2",
        "outputId": "b4f9d0c4-b2be-4a04-8cbc-d91464068f32"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['<start>',\n",
              " 'hi i am interested in your board !',\n",
              " 'hi do you have any questions ?',\n",
              " 'do you know what wood it is made out of ?',\n",
              " 'i do not know specifically but the brand is a gravity skateboard .',\n",
              " 'the wheels seem nice on it but they could be a beter quality . would you accept <price>_-0.2 for the board ?',\n",
              " 'no that offer is too low . the board is pretty much brand new as it s been ridden only or times . the bone bearings are brand new .',\n",
              " 'if i picked it up at your location would you accpet <price>_0.0 ?',\n",
              " 'make it <price>_0.4 and we got a deal .',\n",
              " 'i could do <price>_0.3 if you can gurantee the painting on the front of the board is not scratched up .',\n",
              " 'deal',\n",
              " 'great !',\n",
              " '<offer>',\n",
              " '<accept>']"
            ]
          },
          "metadata": {},
          "execution_count": 173
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "d['train'][1]['ratio']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vGlatVxI0FDp",
        "outputId": "0adc4b2e-d146-47f7-9c35-5de5792fb693"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.45"
            ]
          },
          "metadata": {},
          "execution_count": 174
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "d['train'][1]['dial_acts']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "acXEBYbz0jny",
        "outputId": "b14c8d41-06bc-4689-de4b-be619efd8a86"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['<start>',\n",
              " 'intro',\n",
              " 'unknown',\n",
              " 'inquiry',\n",
              " 'disagree',\n",
              " 'init-price',\n",
              " 'vague-price',\n",
              " 'counter-price',\n",
              " 'counter-price',\n",
              " 'counter-price',\n",
              " 'agree',\n",
              " 'agree',\n",
              " '<offer>',\n",
              " '<accept>']"
            ]
          },
          "metadata": {},
          "execution_count": 175
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "d['train'][1]['strategies']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hVoom61ezh9U",
        "outputId": "7530ed76-8905-4cc5-ef85-a585bda448fa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[['<start>'],\n",
              " ['first_person_singular_count_buyer', 'politeness_buyer_greet'],\n",
              " ['number_of_diff_dic_pos', 'politeness_seller_greet'],\n",
              " ['third_person_singular_buyer', 'factive_count_buyer'],\n",
              " ['first_person_singular_count_seller',\n",
              "  'number_of_diff_dic_pos',\n",
              "  'number_of_diff_dic_neg',\n",
              "  'factive_count_seller'],\n",
              " ['buyer_pos_sentiment',\n",
              "  'third_person_singular_buyer',\n",
              "  'third_person_plural_buyer',\n",
              "  'buyer_propose',\n",
              "  'hedge_count_buyer',\n",
              "  'assertive_count_buyer'],\n",
              " ['seller_neg_sentiment',\n",
              "  'seller_pos_sentiment',\n",
              "  'third_person_singular_seller',\n",
              "  'number_of_diff_dic_pos'],\n",
              " ['first_person_singular_count_buyer',\n",
              "  'third_person_singular_buyer',\n",
              "  'buyer_propose',\n",
              "  'hedge_count_buyer'],\n",
              " ['first_person_plural_count_seller',\n",
              "  'third_person_singular_seller',\n",
              "  'number_of_diff_dic_pos',\n",
              "  'seller_propose'],\n",
              " ['first_person_singular_count_buyer', 'hedge_count_buyer'],\n",
              " [],\n",
              " ['buyer_pos_sentiment'],\n",
              " ['buyer_pos_sentiment'],\n",
              " ['buyer_pos_sentiment']]"
            ]
          },
          "metadata": {},
          "execution_count": 176
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "d['train'][1]['strategies_vec']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZnIWdfSk0u-Y",
        "outputId": "180ed802-bab7-44f2-d67a-a5ede234bfad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "        0., 0., 0., 0., 0., 0., 1.],\n",
              "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 1., 0., 0., 0.,\n",
              "        0., 0., 0., 0., 0., 0., 0.],\n",
              "       [1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0.,\n",
              "        1., 0., 0., 0., 0., 0., 0.],\n",
              "       [0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0.,\n",
              "        0., 0., 0., 0., 0., 0., 0.],\n",
              "       [1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 1., 1., 0.,\n",
              "        1., 0., 0., 0., 0., 0., 0.],\n",
              "       [0., 1., 1., 1., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0.,\n",
              "        0., 0., 0., 1., 1., 0., 0.],\n",
              "       [1., 0., 1., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "        1., 0., 0., 1., 0., 0., 0.],\n",
              "       [0., 0., 1., 1., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0.,\n",
              "        0., 0., 0., 0., 1., 0., 0.],\n",
              "       [1., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "        1., 0., 0., 0., 1., 1., 0.],\n",
              "       [0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0.,\n",
              "        0., 0., 0., 0., 0., 0., 0.],\n",
              "       [1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "        0., 0., 0., 0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "        0., 0., 0., 1., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "        0., 0., 0., 1., 0., 0., 0.],\n",
              "       [1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
              "        0., 0., 0., 1., 0., 0., 0.]])"
            ]
          },
          "metadata": {},
          "execution_count": 177
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "d['train'][0]['toks_space']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MYadwY-E06zB",
        "outputId": "2e85f4a1-386e-4094-c63e-a35a5ed4c0c3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[2, 1, 3],\n",
              " [2,\n",
              "  80,\n",
              "  81,\n",
              "  82,\n",
              "  83,\n",
              "  15,\n",
              "  20,\n",
              "  84,\n",
              "  85,\n",
              "  60,\n",
              "  86,\n",
              "  76,\n",
              "  17,\n",
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              "  68,\n",
              "  88,\n",
              "  89,\n",
              "  29,\n",
              "  90,\n",
              "  60,\n",
              "  91,\n",
              "  92,\n",
              "  3],\n",
              " [2, 89, 93, 85, 94, 95, 96, 97, 98, 76, 99, 98, 100, 101, 25, 17, 3],\n",
              " [2, 102, 87, 94, 103, 89, 60, 91, 92, 94, 104, 14, 98, 105, 106, 3],\n",
              " [2, 94, 107, 15, 108, 94, 84, 109, 29, 16, 70, 110, 17, 3],\n",
              " [2,\n",
              "  111,\n",
              "  112,\n",
              "  113,\n",
              "  55,\n",
              "  94,\n",
              "  114,\n",
              "  89,\n",
              "  115,\n",
              "  116,\n",
              "  92,\n",
              "  89,\n",
              "  117,\n",
              "  118,\n",
              "  68,\n",
              "  119,\n",
              "  29,\n",
              "  90,\n",
              "  17,\n",
              "  3],\n",
              " [2, 120, 55, 121, 95, 98, 122, 17, 3],\n",
              " [2, 123, 124, 17, 120, 17, 3],\n",
              " [2, 4, 3],\n",
              " [2, 6, 3]]"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "l1 = re.findall(\" [0-9]*.[0-9]* | [0-9]+\",' 19 ')\n",
        "l1[0][1:-1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "tzLV_haKGLND",
        "outputId": "cff698b0-40b8-40db-ad5a-4ccbad225053"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'19'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "user=[]\n",
        "agent=[]\n",
        "n_strategy_man = []\n",
        "n_strategy_agent = []\n",
        "d_act = []\n",
        "intent = []\n",
        "slot_value = []\n",
        "values = []\n",
        "\n",
        "t_set = 0\n",
        "\n",
        "L = ['train','valid','test']\n",
        "L_counter = 0\n",
        "\n",
        "for j in L:\n",
        "  for i in d[j]:\n",
        "    if len(user) != len(agent):\n",
        "      # print(\"COUNTER: \",counter)\n",
        "      break\n",
        "\n",
        "    t_set = t_set + 1\n",
        "    print(\"train set: \",t_set)\n",
        "    user.append(t_set)\n",
        "    agent.append(t_set)\n",
        "    n_strategy_man.append(\"nan\")\n",
        "    n_strategy_agent.append(\"nan\")\n",
        "    d_act.append(\"nan\")\n",
        "    intent.append(\"nan\")\n",
        "    slot_value.append(\"nan\")\n",
        "    values.append(\"nan\")\n",
        "\n",
        "    counter = 0\n",
        "    flag = 0\n",
        "\n",
        "    for j in range(len(i['utterance'])):\n",
        "      if len(re.findall(\"<[A-Za-z0-9]*>\", i['utterance'][j])) == 0 or re.findall(\"<[A-Za-z0-9]*>\", i['utterance'][j])[0] == '<price>':\n",
        "        print(\"turn: \",(j//2)+1)\n",
        "        # print(i['utterance'][j])\n",
        "\n",
        "        # utterance ---->\n",
        "        if int(i['agent_list'][j]) == 0 : # User\n",
        "          if counter % 2 == 0:\n",
        "            user.append(i['utterance'][j])\n",
        "            intent.append(i['dial_acts'][j])\n",
        "            n_strategy_man.append(i['strategies'][j])\n",
        "\n",
        "            l = re.findall(\"<price>_[|-]*[0-9]*.[0-9]*\",i['utterance'][j])\n",
        "            if len(l) > 0:\n",
        "              value = l[0][8:]\n",
        "              slot_value.append(\"Price_ratio=\" + str(value))\n",
        "              values.append(value)\n",
        "            else:\n",
        "              value = \"\" \n",
        "              slot_value.append(value) \n",
        "              values.append(value)\n",
        "            \n",
        "\n",
        "            counter += 1\n",
        "          else:\n",
        "            user[-1] = user[-1] + i['utterance'][j] \n",
        "            intent[-1] = intent[-1] + i['dial_acts'][j]\n",
        "            n_strategy_man[-1] = n_strategy_man[-1] + i['strategies'][j]\n",
        "\n",
        "            l = re.findall(\"<price>_[|-]*[0-9]*.[0-9]*\",i['utterance'][j])\n",
        "            if len(l) > 0:\n",
        "              value = l[0][8:]\n",
        "              slot_value[-1] = slot_value[-1] + \", \" +\"Price_ratio=\" + str(value)\n",
        "              values[-1] = values[-1] + \", \" + str(value)\n",
        "            \n",
        "\n",
        "        elif int(i['agent_list'][j]) == 1 : # Agent\n",
        "          if counter % 2 != 0:\n",
        "            agent.append(i['utterance'][j])\n",
        "            d_act.append(i['dial_acts'][j])\n",
        "            n_strategy_agent.append(i['strategies'][j])\n",
        "            counter += 1\n",
        "          else:\n",
        "            if agent[-1] != t_set:\n",
        "              agent[-1] = agent[-1] + i['utterance'][j]\n",
        "              d_act[-1] = d_act[-1] + i['dial_acts'][j]\n",
        "              # print(n_strategy_agent)\n",
        "              # print(\"current strategy: \",i['strategies'][j])\n",
        "              n_strategy_agent[-1] = n_strategy_agent[-1] + i['strategies'][j] \n",
        "            else:\n",
        "              user.append(\"\")\n",
        "              intent.append(\"\")\n",
        "              n_strategy_man.append(\"\")\n",
        "              slot_value.append(\"\")\n",
        "              values.append(\"\")\n",
        "\n",
        "              agent.append(i['utterance'][j])\n",
        "              d_act.append(i['dial_acts'][j])\n",
        "              n_strategy_agent.append(i['strategies'][j])\n",
        "\n",
        "              counter += 2 \n",
        "      \n",
        "      if j == len(i['utterance'])-1:\n",
        "        # print(\"HEY I'M HERE\")\n",
        "        if counter % 2 != 0:\n",
        "          agent.append(\"\")\n",
        "          d_act.append(\"\")\n",
        "          n_strategy_agent.append([\"\"])\n",
        "          counter = 0\n",
        "        else:\n",
        "          # print(\"counter: \",counter)\n",
        "          counter = 0  \n",
        "\n",
        "final_df = pd.DataFrame({'User': user, \"Agent\": agent, \"Slot-Value\": slot_value, \"Value\": values, \n",
        "                         \"Negotiation Strategy User\": n_strategy_man, \"Negotiation Strategy Agent\": n_strategy_agent, \n",
        "                         \"Agent Dialogue Act\": d_act,\n",
        "                         \"Intent\": intent})\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "px5u9ZkZ-xIb",
        "outputId": "0253e115-61fd-41b3-960d-3c5826bbbe19"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
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            "train set:  5954\n",
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            "train set:  5955\n",
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            "turn:  4\n",
            "turn:  5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "final_df.to_csv(\"CraigListBergain.csv\",index=False)"
      ],
      "metadata": {
        "id": "lV04m2k84Rdd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('CraigListBergain.csv')\n",
        "data.iloc[5:20,:]"
      ],
      "metadata": {
        "id": "lpkTFKlI1vOJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 754
        },
        "outputId": "7656f79c-ee11-4606-9b49-c6536592714a"
      },
      "execution_count": null,
      "outputs": [
        {
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              "                                                 User  \\\n",
              "5                                                   2   \n",
              "6                  hi i am interested in your board !   \n",
              "7           do you know what wood it is made out of ?   \n",
              "8   the wheels seem nice on it but they could be a...   \n",
              "9   if i picked it up at your location would you a...   \n",
              "10  i could do <price>_0.3 if you can gurantee the...   \n",
              "11                                            great !   \n",
              "12                                                  3   \n",
              "13                                i would love to buy   \n",
              "14          im on a budget so i could do <price>_-0.1   \n",
              "15                                     i will take it   \n",
              "16                                                  4   \n",
              "17                                              hello   \n",
              "18                             how old is the couch ?   \n",
              "19  as you know the value goes down as soon as the...   \n",
              "\n",
              "                                                Agent        Slot-Value Value  \\\n",
              "5                                                   2               NaN   NaN   \n",
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              "7   i do not know specifically but the brand is a ...               NaN   NaN   \n",
              "8   no that offer is too low . the board is pretty...  Price_ratio=-0.2  -0.2   \n",
              "9             make it <price>_0.4 and we got a deal .   Price_ratio=0.0   0.0   \n",
              "10                                               deal   Price_ratio=0.3   0.3   \n",
              "11                                                NaN               NaN   NaN   \n",
              "12                                                  3               NaN   NaN   \n",
              "13                         sure ! what s your price ?               NaN   NaN   \n",
              "14  how about <price>_-0.0 and i ll wave the depos...  Price_ratio=-0.1  -0.1   \n",
              "15                                            great !               NaN   NaN   \n",
              "16                                                  4               NaN   NaN   \n",
              "17                                                 hi               NaN   NaN   \n",
              "18                        it is practically brand new               NaN   NaN   \n",
              "19  well as i said it has nearly no wear and was i...  Price_ratio=-0.3  -0.3   \n",
              "\n",
              "                            Negotiation Strategy User  \\\n",
              "5                                                 NaN   \n",
              "6   ['first_person_singular_count_buyer', 'politen...   \n",
              "7   ['third_person_singular_buyer', 'factive_count...   \n",
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              "15  ['first_person_singular_count_buyer', 'third_p...   \n",
              "16                                                NaN   \n",
              "17                         ['politeness_buyer_greet']   \n",
              "18                                                 []   \n",
              "19  ['buyer_pos_sentiment', 'first_person_singular...   \n",
              "\n",
              "                           Negotiation Strategy Agent Agent Dialogue Act  \\\n",
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              "6   ['number_of_diff_dic_pos', 'politeness_seller_...            unknown   \n",
              "7   ['first_person_singular_count_seller', 'number...           disagree   \n",
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              "10                                                 []              agree   \n",
              "11                                               ['']                NaN   \n",
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